cnn.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# CNN des chiffres et des lettres\n",
    "\n",
    "Satger Aurélien et Dauliac Julien"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Mode dev:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The autoreload extension is already loaded. To reload it, use:\n",
      "  %reload_ext autoreload\n"
     ]
    }
   ],
   "source": [
    "%load_ext autoreload\n",
    "%autoreload 2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "## Import librairies 📚"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import keras"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import datetime\n",
    "\n",
    "cache_dir = \".cache\" \n",
    "\n",
    "def get_directory(name, cache_dir=cache_dir):\n",
    "    return f\"{cache_dir}/{name}\"\n",
    "\n",
    "def get_asset_directory(name):\n",
    "    return f\"assets/{name}\"\n",
    "\n",
    "def get_log_directory(name, cache_dir=cache_dir):\n",
    "    directory_name = get_directory(name)\n",
    "    log_dir = f\"{directory_name}/log\"\n",
    "    return log_dir\n",
    "\n",
    "def get_fit_directory(name, cache_dir=cache_dir):\n",
    "    directory_name = get_directory(name)\n",
    "    log_dir = f\"{directory_name}/fit\"\n",
    "    return log_dir\n",
    "\n",
    "def get_project_name(section_label):\n",
    "    return f\"CNN-{section_label}\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['a', 'b', 'c', 'd', 'e', 'f', 'g', 'h', 'i', 'j', 'k', 'l', 'm', 'n', 'o', 'p', 'q', 'r', 's', 't', 'u', 'v', 'w', 'x', 'y', 'z']\n"
     ]
    }
   ],
   "source": [
    "import string\n",
    "alphabet = list(string.ascii_lowercase)\n",
    "print(alphabet)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "## Chargement des données 💾"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 4.19 s, sys: 1.67 s, total: 5.86 s\n",
      "Wall time: 2min 3s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "letters_train = pd.read_csv(\n",
    "    'https://www.labri.fr/perso/zemmari/datasets/emnist/emnist-letters-train.csv',\n",
    "    header=None\n",
    ")\n",
    "\n",
    "letters_test = pd.read_csv(\n",
    "    'https://www.labri.fr/perso/zemmari/datasets/emnist/emnist-letters-test.csv',\n",
    "    header=None\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
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     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "letters_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
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     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "letters_test.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Preprocess des données 🧮"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def shape_data(x):\n",
    "    x = np.reshape(x, (x.shape[0], 28, 28))\n",
    "    \n",
    "    x = x.astype('float32')\n",
    "    x  = x / 255\n",
    "    return x\n",
    "\n",
    "def categorize_data(y, categorical_count):\n",
    "    return keras.utils.to_categorical(y, categorical_count)\n",
    "\n",
    "def preprocess_data(x, y, categorical_count=26):    \n",
    "    x = shape_data(x)\n",
    "    y = categorize_data(y, categorical_count)\n",
    "    return x, y\n",
    "\n",
    "def preprocess_data_instruction(letters, categorical_count=26):\n",
    "    \"\"\"\n",
    "    It takes the dataframe of letters and returns the x and y data in the format that we need for our\n",
    "    neural network\n",
    "    \n",
    "    :param letters: the dataframe containing the data\n",
    "    :return: x is the array of images and y is the array of labels\n",
    "    \"\"\"\n",
    "\n",
    "    x = np.array(letters.iloc[:, 1:785])\n",
    "    y = np.array(letters.iloc[:, 0])\n",
    "    y = y - 1\n",
    "    return x, y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "(x_train_unprocessed, y_train_unprocessed) = preprocess_data_instruction(letters_train)\n",
    "(x_test_unprocessed, y_test_unprocessed) = preprocess_data_instruction(letters_test)\n",
    "(x_train, y_train) = preprocess_data(x_train_unprocessed, y_train_unprocessed)\n",
    "(x_test, y_test) = preprocess_data(x_test_unprocessed, y_test_unprocessed)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def print_shape(x_train, y_train, x_test, y_test):\n",
    "    print('Train: x=%s, y=%s' % (x_train.shape, y_train.shape))\n",
    "    print('Test: x=%s, y=%s' % (x_test.shape, y_test.shape))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train: x=(88800, 28, 28), y=(88800, 26)\n",
      "Test: x=(14800, 28, 28), y=(14800, 26)\n"
     ]
    }
   ],
   "source": [
    "print_shape(x_train, y_train, x_test, y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exploration 🧭"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_9_random_images(x, y, references=alphabet):\n",
    "    \"\"\"\n",
    "    It plots 9 random images from the dataset\n",
    "    \n",
    "    :param x: the images\n",
    "    :param y: the labels for the images\n",
    "    \"\"\"\n",
    "    \n",
    "    _, axes = plt.subplots(3, 3, figsize=(7, 7))\n",
    "\n",
    "    for i in range(9):\n",
    "        ax = axes[i // 3][i%3]\n",
    "        idx = np.random.randint(0, len(x))\n",
    "        ax.imshow(x[idx])\n",
    "        ax.set_title(references[np.argmax(y[idx])])\n",
    "\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 700x700 with 9 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_9_random_images(x_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Nous allons afficher et comparer nos données de test et nos données d'entrainement:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_train_vs_test(y_train, y_test, labels=alphabet):\n",
    "    _, ax = plt.subplots(figsize=(9, 5))\n",
    "    shape_len = len(labels)\n",
    "    ax.bar(range(shape_len), y_train.sum(axis=0), label=\"train\")\n",
    "    ax.bar(range(shape_len), y_test.sum(axis=0), label=\"test\")\n",
    "    ax.legend()\n",
    "    ax.set_xticks(range(shape_len))\n",
    "    ax.set_xticklabels(labels)\n",
    "    ax.set_title(\"Nombre de données y par lettres\")\n",
    "\n",
    "    ax.legend()\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 900x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_train_vs_test(y_train, y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Nous allons donc faire un fix en entraînant jusqu'au `s`\n",
    "Nous ne serons pas capable de reconnaitre les lettres allant de `s` à `z`.\n",
    " \n",
    "Cependant le jeu de test et d'entrainement étant fourni dans le sujet, nous choisissons de les garder indépendantes.\n",
    "On retire donc les données non annotées.\n",
    " \n",
    "On peut également noter un manque de données de tests pour le `s`. Notre modèle reconnaitra donc moins bien les `s` des autres lettres.\n",
    "\n",
    "Il aurait également été possible d'alimenter notre jeu de test avec des données du jeux d'entrainement. Cela aurait été au risque de biaiser notre jeu d'entrainement ou de sortir de la consigne du sujet."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def fixed_data(\n",
    "    x_train=x_train,\n",
    "    y_train=y_train,\n",
    "    x_test=x_test,\n",
    "    y_tests=y_test,\n",
    "    alphabet=alphabet\n",
    "):\n",
    "    letter_s_index = 18\n",
    "    alphabet_fixed = alphabet[0:letter_s_index+1]\n",
    "    y_test_fixed = y_test[:, 0:letter_s_index+1]\n",
    "    y_train_fixed = y_train[:, 0:letter_s_index+1]\n",
    "    return x_train, y_train_fixed, x_test, y_test_fixed, alphabet_fixed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train_fixed, y_train_fixed, x_test_fixed, y_test_fixed, alphabet_fixed = fixed_data()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "def print_train_shape(x_train, y_train):\n",
    "    if x_train.shape[0] == y_train.shape[0]:\n",
    "        print(\"x_train \" + str(x_train.shape[0]) + \" == y_train \" + str(y_train.shape[0]))\n",
    "    elif x_train.shape[0] < y_train.shape[0]:\n",
    "        print(\"x_train \" + str(x_train.shape[0]) + \" < y_train \" + str(y_train.shape[0]))\n",
    "    else:\n",
    "        print(\"x_train \" + str(x_train.shape[0]) + \" > y_train \" + str(y_train.shape[0]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "nos données d'entrainement doivent avoir la même quantitée de données."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "x_train 88800 == y_train 88800\n"
     ]
    }
   ],
   "source": [
    "print_train_shape(x_train_fixed, y_train_fixed)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 900x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_train_vs_test(y_train_fixed, y_test_fixed, alphabet_fixed)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "x_train 88800 == y_train 88800\n"
     ]
    }
   ],
   "source": [
    "print_train_shape(x_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "On peut voir que nos jeux de test et d'entrainement sont désormais proportionnés 🎊."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Fit un modèle simple 🚀"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from keras.models import Sequential\n",
    "from keras.layers import Conv2D, MaxPooling2D, Flatten, Dense, Dropout\n",
    "\n",
    "simple_model_section_label = \"fit-simple-model\"\n",
    "simple_model_log_dir = get_log_directory(simple_model_section_label)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "num_pixels = x_train_fixed.shape[1] * x_train_fixed.shape[2]\n",
    "num_letters = y_train_fixed.shape[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(19, 784)"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "num_letters, num_pixels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_simple_model(num_pixels, num_letters):\n",
    "    model = Sequential()\n",
    "    \n",
    "    model.add(Conv2D(32, kernel_size=(3, 3), activation='relu', input_shape=(28,28,1)))\n",
    "    model.add(MaxPooling2D(pool_size=(3, 3)))\n",
    "        \n",
    "    model.add(Flatten())\n",
    "    \n",
    "    model.add(Dropout(0.25))\n",
    "    model.add(Dense(num_pixels, input_dim=num_pixels, activation='sigmoid'))\n",
    "\n",
    "    model.add(Dropout(0.25))\n",
    "\n",
    "    # # Ajout d'une couche de classification en sortie avec 26 neurones (une pour chaque lettre de l'alphabet)\n",
    "    model.add(Dense(num_letters, activation='softmax'))\n",
    "\n",
    "    return model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " conv2d (Conv2D)             (None, 26, 26, 32)        320       \n",
      "                                                                 \n",
      " max_pooling2d (MaxPooling2D  (None, 8, 8, 32)         0         \n",
      " )                                                               \n",
      "                                                                 \n",
      " flatten (Flatten)           (None, 2048)              0         \n",
      "                                                                 \n",
      " dropout (Dropout)           (None, 2048)              0         \n",
      "                                                                 \n",
      " dense (Dense)               (None, 784)               1606416   \n",
      "                                                                 \n",
      " dropout_1 (Dropout)         (None, 784)               0         \n",
      "                                                                 \n",
      " dense_1 (Dense)             (None, 19)                14915     \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 1,621,651\n",
      "Trainable params: 1,621,651\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-12-29 12:43:15.094105: W tensorflow/compiler/xla/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libcuda.so.1'; dlerror: libcuda.so.1: cannot open shared object file: No such file or directory\n",
      "2022-12-29 12:43:15.094331: W tensorflow/compiler/xla/stream_executor/cuda/cuda_driver.cc:265] failed call to cuInit: UNKNOWN ERROR (303)\n",
      "2022-12-29 12:43:15.094345: I tensorflow/compiler/xla/stream_executor/cuda/cuda_diagnostics.cc:156] kernel driver does not appear to be running on this host (pop-os): /proc/driver/nvidia/version does not exist\n",
      "2022-12-29 12:43:15.095083: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations:  AVX2 AVX512F AVX512_VNNI FMA\n",
      "To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\n"
     ]
    }
   ],
   "source": [
    "simple_model = get_simple_model(num_pixels, num_letters)\n",
    "simple_model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "simple_model.compile(\n",
    "    loss='categorical_crossentropy',\n",
    "    optimizer='adam',\n",
    "    metrics=['accuracy']\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/15\n",
      "278/278 [==============================] - 12s 40ms/step - loss: 1.3639 - accuracy: 0.3253 - val_loss: 1.0347 - val_accuracy: 0.4908\n",
      "Epoch 2/15\n",
      "278/278 [==============================] - 11s 40ms/step - loss: 1.1008 - accuracy: 0.4170 - val_loss: 0.8897 - val_accuracy: 0.4960\n",
      "Epoch 3/15\n",
      "278/278 [==============================] - 12s 43ms/step - loss: 1.0328 - accuracy: 0.4409 - val_loss: 0.8123 - val_accuracy: 0.7582\n",
      "Epoch 4/15\n",
      "278/278 [==============================] - 12s 43ms/step - loss: 0.9789 - accuracy: 0.4625 - val_loss: 0.7973 - val_accuracy: 0.5268\n",
      "Epoch 5/15\n",
      "278/278 [==============================] - 11s 41ms/step - loss: 0.9287 - accuracy: 0.4755 - val_loss: 0.7510 - val_accuracy: 0.5546\n",
      "Epoch 6/15\n",
      "278/278 [==============================] - 12s 43ms/step - loss: 0.8880 - accuracy: 0.4888 - val_loss: 0.7324 - val_accuracy: 0.8095\n",
      "Epoch 7/15\n",
      "278/278 [==============================] - 11s 41ms/step - loss: 0.8488 - accuracy: 0.5010 - val_loss: 0.6644 - val_accuracy: 0.5641\n",
      "Epoch 8/15\n",
      "278/278 [==============================] - 12s 42ms/step - loss: 0.8075 - accuracy: 0.5149 - val_loss: 0.6308 - val_accuracy: 0.8295\n",
      "Epoch 9/15\n",
      "278/278 [==============================] - 12s 44ms/step - loss: 0.7779 - accuracy: 0.5230 - val_loss: 0.6222 - val_accuracy: 0.5736\n",
      "Epoch 10/15\n",
      "278/278 [==============================] - 12s 42ms/step - loss: 0.7533 - accuracy: 0.5318 - val_loss: 0.5865 - val_accuracy: 0.8416\n",
      "Epoch 11/15\n",
      "278/278 [==============================] - 12s 44ms/step - loss: 0.7310 - accuracy: 0.5374 - val_loss: 0.5605 - val_accuracy: 0.5889\n",
      "Epoch 12/15\n",
      "278/278 [==============================] - 11s 41ms/step - loss: 0.7114 - accuracy: 0.5440 - val_loss: 0.5568 - val_accuracy: 0.5891\n",
      "Epoch 13/15\n",
      "278/278 [==============================] - 12s 43ms/step - loss: 0.6977 - accuracy: 0.5465 - val_loss: 0.5293 - val_accuracy: 0.5939\n",
      "Epoch 14/15\n",
      "278/278 [==============================] - 12s 44ms/step - loss: 0.6792 - accuracy: 0.5506 - val_loss: 0.5443 - val_accuracy: 0.5883\n",
      "Epoch 15/15\n",
      "278/278 [==============================] - 11s 41ms/step - loss: 0.6711 - accuracy: 0.5555 - val_loss: 0.5122 - val_accuracy: 0.5995\n",
      "CPU times: user 13min 15s, sys: 11.1 s, total: 13min 26s\n",
      "Wall time: 2min 56s\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x7f344ad77910>"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "import tensorflow as tf\n",
    "validation_split=0.2\n",
    "\n",
    "simple_model_tensorboard_callback = tf.keras.callbacks.TensorBoard(\n",
    "    log_dir=simple_model_log_dir,\n",
    "    histogram_freq=1)\n",
    "\n",
    "simple_model.fit(\n",
    "    x_train_fixed,\n",
    "    y_train_fixed,\n",
    "    epochs=15,\n",
    "    batch_size=256,\n",
    "    validation_split=validation_split,\n",
    "    callbacks=[simple_model_tensorboard_callback])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def evaluate(model, x, y):\n",
    "    score = model.evaluate(\n",
    "        x_test_fixed,\n",
    "        y_test_fixed,\n",
    "        batch_size=128\n",
    "    )\n",
    "\n",
    "    print(\"Test loss:\", score[0])\n",
    "    print(\"Test accuracy:\", score[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "116/116 [==============================] - 1s 4ms/step - loss: 0.7115 - accuracy: 0.8167\n",
      "Test loss: 0.7115131616592407\n",
      "Test accuracy: 0.8166891932487488\n"
     ]
    }
   ],
   "source": [
    "evaluate(simple_model, x_test_fixed, y_test_fixed)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The tensorboard extension is already loaded. To reload it, use:\n",
      "  %reload_ext tensorboard\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Reusing TensorBoard on port 6006 (pid 14490), started 0:05:31 ago. (Use '!kill 14490' to kill it.)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "\n",
       "      <iframe id=\"tensorboard-frame-b6601c7388167631\" width=\"100%\" height=\"800\" frameborder=\"0\">\n",
       "      </iframe>\n",
       "      <script>\n",
       "        (function() {\n",
       "          const frame = document.getElementById(\"tensorboard-frame-b6601c7388167631\");\n",
       "          const url = new URL(\"/\", window.location);\n",
       "          const port = 6006;\n",
       "          if (port) {\n",
       "            url.port = port;\n",
       "          }\n",
       "          frame.src = url;\n",
       "        })();\n",
       "      </script>\n",
       "    "
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%load_ext tensorboard\n",
    "%tensorboard --logdir {simple_model_log_dir}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "C'est pas trop mal, mais on peut faire mieux 🔝\n",
    "\n",
    "On peut noter :\n",
    "- la loss descend de manière constante,\n",
    "- l'accuracy de validation augmente et diminue en suivant des cycles. On peut suposer que se limiter à un nombre d'épochs entre 2 et 6 peut être suffisant pour explorer les hyper paramètres dans un premier temps."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3 Benchmarks des hyper paramètres et variation d'architecture 🎛️\n",
    "\n",
    "Nous allons utiliser `keras_tuner` pour tester plusieurs combinaisons d'hyper paramètres et de modèles.\n",
    "\n",
    "Nous explorerons les pistes suivantes:\n",
    "- variation des `kernel_size` des couches de convolution afin de tester différents niveaux de détails\n",
    "- ajout d'une à 3 couche cachées `Dense`\n",
    "- ajout de couches de `Dropout` ou non pour limiter le sur-apprentissage\n",
    "    - variation des valeurs de dropout indépendantes pour chaques couches\n",
    "- changement des fonctions d'activation\n",
    "\n",
    "Edit:\n",
    "- Après plusieurs essaies, nous avons pu constater que l'hyper paramètre `kernel_size` n'avait pas d'impacte positif sur la performance de notre modèle, nous garderons donc la même valeur que dans l'approche simple. Nous retirons l'option du `tunner` afin de limiter le nombre d'hyper paramètres à explorer."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import keras_tuner\n",
    "\n",
    "def build_model(hp, num_pixels=num_pixels, num_classes=num_letters):\n",
    "    model = Sequential()\n",
    "    \n",
    "    model.add(Conv2D(\n",
    "        32,\n",
    "        kernel_size=(3,3), \n",
    "        activation='relu',\n",
    "        input_shape=(28,28,1)\n",
    "    ))\n",
    "    model.add(MaxPooling2D(pool_size=(2, 2)))\n",
    "        \n",
    "    model.add(Flatten())\n",
    "\n",
    "    model.add(Dense(num_pixels, activation='sigmoid'))\n",
    "    if hp.Boolean(\"dropout\"):\n",
    "        dropout = hp.Float(\n",
    "            \"dropout_first\",\n",
    "            min_value=0.1,\n",
    "            max_value=0.9,\n",
    "            sampling=\"log\"\n",
    "        )\n",
    "        model.add(Dropout(dropout))\n",
    "\n",
    "    for i in range(1, hp.Int(\"num_layers\", 2, 4)):\n",
    "        units=hp.Int(\n",
    "            f\"dense_{i}\",\n",
    "            min_value=num_classes,\n",
    "            max_value=num_pixels,\n",
    "            step=2\n",
    "        )\n",
    "        model.add(Dense(units, activation=\"sigmoid\"))\n",
    "        \n",
    "        if hp.Boolean(\"dropout\"):\n",
    "            dropout = hp.Float(\n",
    "                f\"dropout_{i}\",\n",
    "                min_value=0.1,\n",
    "                max_value=0.9,\n",
    "                sampling=\"log\"\n",
    "            )\n",
    "            model.add(Dropout(dropout))\n",
    "    \n",
    "    model.add(Dense(num_classes, activation='softmax'))\n",
    "        \n",
    "    model.compile(\n",
    "        loss='categorical_crossentropy',\n",
    "        optimizer='adam',\n",
    "        metrics=['accuracy']\n",
    "    )\n",
    "    \n",
    "    return model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<keras.engine.sequential.Sequential at 0x7f33f44cd780>"
      ]
     },
     "execution_count": 303,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "build_model(keras_tuner.HyperParameters())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(19, 784)"
      ]
     },
     "execution_count": 304,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "num_letters, num_pixels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "tunner_section_label = \"tunner-letter\"\n",
    "tunner_log_dir = get_log_directory(tunner_section_label)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_tensorboard_callback(log_dir):\n",
    "    return tf.keras.callbacks.TensorBoard(\n",
    "        log_dir=log_dir,\n",
    "        histogram_freq=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "#### Random tunner 🎲"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tunner-letter-random .cache/tunner-letter-random/log\n"
     ]
    }
   ],
   "source": [
    "random_tunner_section_label = tunner_section_label + \"-random\"\n",
    "random_tunner_log_dir = get_log_directory(random_tunner_section_label)\n",
    "print(random_tunner_section_label, random_tunner_log_dir)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_random_tunner(section_label, objective=\"val_accuracy\", max_trials=10):\n",
    "    return keras_tuner.RandomSearch(\n",
    "        hypermodel=build_model,\n",
    "        objective=objective,\n",
    "        max_trials=max_trials,\n",
    "        seed=1,\n",
    "        executions_per_trial=1,\n",
    "        overwrite=True,\n",
    "        directory=get_fit_directory(section_label),\n",
    "        project_name=get_project_name(section_label))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "#### Early stopping 🛑\n",
    "- Nous utiliserons une fonction de callback `EarlyStopping` qui permet d'arrêter l'entrainement de manière précipitée.\n",
    "Cette méthode nous aidera à trouver l'hyper paramètre d'`epochs` adapté à la situation\n",
    "- Comme les cycles d'augmentation et de diminution de la validation se déroulent sur plus ou moins 3 cycles, nous définirons une patience de 4 épochs pour `EarlyStopping`\n",
    "- Après plusieurs essaies, nous avons pu constater que l'`EarlyStopping` arrète toujours l'entrainement et ce dans un temps raisonable: `[5-40]` `epochs`. C'est une méthode efficasse qui nous permet de trouver un nombre d'`epochs` optimisé."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_early_stoping():\n",
    "    return keras.callbacks.EarlyStopping(\n",
    "        monitor='val_loss',\n",
    "        patience=4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def benchmark(tunner, log_dir, x_train, y_train, epochs=4, early_stoping=True):\n",
    "    early_stoping_callback = get_early_stoping()\n",
    "    tensorboard_callback = get_tensorboard_callback(log_dir)\n",
    "    callbacks = [tensorboard_callback, early_stoping_callback]\n",
    "    tunner.search_space_summary()\n",
    "    tunner.search(\n",
    "        x_train,\n",
    "        y_train,\n",
    "        validation_split=0.2,\n",
    "        epochs=epochs,\n",
    "        callbacks=callbacks,\n",
    "    )\n",
    "    return tunner"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Nous utiliserons un faible nombre d'`epochs` (4) afin d'obtenir une idée des parametres a ajuster."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "random_tunner = get_random_tunner(random_tunner_section_label, max_trials=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Trial 1 Complete [00h 18m 07s]\n",
      "val_accuracy: 0.6011261343955994\n",
      "\n",
      "Best val_accuracy So Far: 0.6011261343955994\n",
      "Total elapsed time: 00h 18m 07s\n",
      "INFO:tensorflow:Oracle triggered exit\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras_tuner.tuners.randomsearch.RandomSearch at 0x7f340614d360>"
      ]
     },
     "execution_count": 519,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "benchmark(\n",
    "    random_tunner,\n",
    "    random_tunner_log_dir,\n",
    "    x_train_fixed,\n",
    "    y_train_fixed,\n",
    "    epochs=17)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Analysons les résultats en utilisant tensorboard:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "      <iframe id=\"tensorboard-frame-7b41cd2d31bd18e5\" width=\"100%\" height=\"800\" frameborder=\"0\">\n",
       "      </iframe>\n",
       "      <script>\n",
       "        (function() {\n",
       "          const frame = document.getElementById(\"tensorboard-frame-7b41cd2d31bd18e5\");\n",
       "          const url = new URL(\"/\", window.location);\n",
       "          const port = 6007;\n",
       "          if (port) {\n",
       "            url.port = port;\n",
       "          }\n",
       "          frame.src = url;\n",
       "        })();\n",
       "      </script>\n",
       "    "
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%tensorboard --logdir {random_tunner_log_dir}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Hyperband tunner 🗺️\n",
    "\n",
    "Cependant une exploration aléatoire des hyper paramètres n'est pas la solution la plus efficace dans notre cas.\n",
    "\n",
    "C'est une solution qui repose sur la chance et utilise une recherche aveugle.\n",
    "\n",
    "Nous allons utiliser un tunner hyperband qui permet d'apprendre des essaies précédents, contrairement à la recherche aléatoire qui est une recherche aveugle.\n",
    "\n",
    "- Il existe également une approche bayesien de recherche des hyper paramètres qui cependant ne marche pas à cause des hyperparamètres de choix ou booleen. Nous exclurons cette possibilité. \n",
    "\n",
    "- Nous augmenterons le nombre d'essais afin d'avoir une plus grande idée des hyper paramètres qui ont un impacte significatif sur notre modèle.\n",
    "- On désactivera par la suite ceux n'ayant pas d'impacte afin d'affiner nos recherches."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tunner-letter-hyperband .cache/tunner-letter-hyperband/log\n"
     ]
    }
   ],
   "source": [
    "hyperband_tunner_section_label = tunner_section_label + \"-hyperband\"\n",
    "hyperband_tunner_log_dir = get_log_directory(hyperband_tunner_section_label)\n",
    "print(hyperband_tunner_section_label, hyperband_tunner_log_dir)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from keras_tuner.tuners import Hyperband\n",
    "\n",
    "def get_hyperband_tunner(section_label, max_epochs=10, factor=3, objective=\"val_accuracy\", hp=keras_tuner.HyperParameters(), build_model=build_model):\n",
    "    hp.Fixed(\"dropout\", value=True)\n",
    "    \n",
    "    return Hyperband(\n",
    "        hypermodel=build_model,\n",
    "        objective=objective,\n",
    "        hyperparameters=hp,\n",
    "        tune_new_entries=True,50 nuances de grey\n",
    "        factor=factor,\n",
    "        max_epochs=max_epochs,\n",
    "        overwrite=True,\n",
    "        seed=1,\n",
    "        directory=get_fit_directory(section_label),\n",
    "        project_name=get_project_name(section_label))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Trial 10 Complete [00h 08m 23s]\n",
      "val_accuracy: 0.8842905163764954\n",
      "\n",
      "Best val_accuracy So Far: 0.8842905163764954\n",
      "Total elapsed time: 00h 53m 59s\n",
      "INFO:tensorflow:Oracle triggered exit\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras_tuner.tuners.hyperband.Hyperband at 0x7f3405c67e20>"
      ]
     },
     "execution_count": 510,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hyperband_tunner = get_hyperband_tunner(\n",
    "    hyperband_tunner_section_label,\n",
    "    max_epochs=6)\n",
    "\n",
    "benchmark(\n",
    "    hyperband_tunner,\n",
    "    hyperband_tunner_log_dir,\n",
    "    x_train_fixed,\n",
    "    y_train_fixed)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "      <iframe id=\"tensorboard-frame-412f9ea7a85dba76\" width=\"100%\" height=\"800\" frameborder=\"0\">\n",
       "      </iframe>\n",
       "      <script>\n",
       "        (function() {\n",
       "          const frame = document.getElementById(\"tensorboard-frame-412f9ea7a85dba76\");\n",
       "          const url = new URL(\"/\", window.location);\n",
       "          const port = 6006;\n",
       "          if (port) {\n",
       "            url.port = port;\n",
       "          }\n",
       "          frame.src = url;\n",
       "        })();\n",
       "      </script>\n",
       "    "
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%tensorboard --logdir {hyperband_tunner_log_dir}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4 Meilleur modèle 🥇\n",
    "Nous allons récupérer et analyser le meilleur modèle retourné par le tunner keras.\n",
    "\n",
    "Nous avons essayé plusieurs configurations et ajout de couches:\n",
    "- Avec Couches de convolutions en plus,\n",
    "- sans le dropout,\n",
    "- avec différentes possibilités sur les fonctions d'activation.\n",
    "\n",
    "Cependant la configuration actuelle est celle permettant d'obtenir les meilleurs résultats."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tunner-letter-hyperband-best-model .cache/tunner-letter-hyperband-best-model/log\n"
     ]
    }
   ],
   "source": [
    "best_model_section_label = hyperband_tunner_section_label + \"-best-model\"\n",
    "best_model_log_dir = get_log_directory(best_model_section_label)\n",
    "print(best_model_section_label, best_model_log_dir)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "best_model = hyperband_tunner.get_best_models()[0]\n",
    "best_hyperparameters = hyperband_tunner.get_best_hyperparameters()[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "116/116 [==============================] - 1s 8ms/step - loss: 0.5385 - accuracy: 0.8496\n",
      "Test loss: 0.5385295152664185\n",
      "Test accuracy: 0.8495945930480957\n"
     ]
    }
   ],
   "source": [
    "score = best_model.evaluate(\n",
    "    x_test_fixed,\n",
    "    y_test_fixed,\n",
    "    batch_size=128\n",
    ")\n",
    "\n",
    "print(\"Test loss:\", score[0])\n",
    "print(\"Test accuracy:\", score[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Detecting that an object or model or tf.train.Checkpoint is being deleted with unrestored values. See the following logs for the specific values in question. To silence these warnings, use `status.expect_partial()`. See https://www.tensorflow.org/api_docs/python/tf/train/Checkpoint#restorefor details about the status object returned by the restore function.\n",
      "WARNING:tensorflow:Value in checkpoint could not be found in the restored object: (root).optimizer._variables.1\n",
      "WARNING:tensorflow:Value in checkpoint could not be found in the restored object: (root).optimizer._variables.2\n",
      "WARNING:tensorflow:Value in checkpoint could not be found in the restored object: (root).optimizer._variables.3\n",
      "WARNING:tensorflow:Value in checkpoint could not be found in the restored object: (root).optimizer._variables.4\n",
      "WARNING:tensorflow:Value in checkpoint could not be found in the restored object: (root).optimizer._variables.5\n",
      "WARNING:tensorflow:Value in checkpoint could not be found in the restored object: (root).optimizer._variables.6\n",
      "WARNING:tensorflow:Value in checkpoint could not be found in the restored object: (root).optimizer._variables.7\n",
      "WARNING:tensorflow:Value in checkpoint could not be found in the restored object: (root).optimizer._variables.8\n",
      "WARNING:tensorflow:Value in checkpoint could not be found in the restored object: (root).optimizer._variables.9\n",
      "WARNING:tensorflow:Value in checkpoint could not be found in the restored object: (root).optimizer._variables.10\n",
      "WARNING:tensorflow:Value in checkpoint could not be found in the restored object: (root).optimizer._variables.11\n",
      "WARNING:tensorflow:Value in checkpoint could not be found in the restored object: (root).optimizer._variables.12\n",
      "WARNING:tensorflow:Value in checkpoint could not be found in the restored object: (root).optimizer._variables.13\n",
      "WARNING:tensorflow:Value in checkpoint could not be found in the restored object: (root).optimizer._variables.14\n",
      "WARNING:tensorflow:Value in checkpoint could not be found in the restored object: (root).optimizer._variables.15\n",
      "WARNING:tensorflow:Value in checkpoint could not be found in the restored object: (root).optimizer._variables.16\n",
      "WARNING:tensorflow:Value in checkpoint could not be found in the restored object: (root).optimizer._variables.17\n",
      "WARNING:tensorflow:Value in checkpoint could not be found in the restored object: (root).optimizer._variables.18\n",
      "WARNING:tensorflow:Value in checkpoint could not be found in the restored object: (root).optimizer._variables.19\n",
      "WARNING:tensorflow:Value in checkpoint could not be found in the restored object: (root).optimizer._variables.20\n",
      "WARNING:tensorflow:Value in checkpoint could not be found in the restored object: (root).optimizer._variables.21\n",
      "WARNING:tensorflow:Value in checkpoint could not be found in the restored object: (root).optimizer._variables.22\n",
      "WARNING:tensorflow:Value in checkpoint could not be found in the restored object: (root).optimizer._variables.23\n",
      "WARNING:tensorflow:Value in checkpoint could not be found in the restored object: (root).optimizer._variables.24\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 439,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from keras.utils import plot_model\n",
    "from IPython.display import SVG\n",
    "from keras.utils.vis_utils import model_to_dot\n",
    "\n",
    "best_model_plot = hyperband_tunner.get_best_models()[0]\n",
    "plot_model(best_model_plot, to_file=\"model.png\", show_shapes=True, show_layer_names=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Comme on peut le voir ce sont les modèles qui ont le plus de couches qui sont les plus performants.\n",
    "\n",
    "C'est une heuristique que l'on avait évoqué en cours et il n'y a rien de très étonnant à cela.\n",
    "\n",
    "On remarque l'absence de `dense_3` ce qui signifie que ce n'est pas le nombre maximum de couche qui est le plus optimisé dans notre cas. Cela rentre un peu en contradiction avec ce que nous avions évoqué en cours à que plus on a de couches, plus le modèle est performant.\n",
    "\n",
    "Voici la liste des hyper paramètes donnants les meilleurs résultats."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>dropout</th>\n",
       "      <th>dropout_first</th>\n",
       "      <th>num_layers</th>\n",
       "      <th>dense_1</th>\n",
       "      <th>dropout_1</th>\n",
       "      <th>dense_2</th>\n",
       "      <th>dropout_2</th>\n",
       "      <th>tuner/epochs</th>\n",
       "      <th>tuner/initial_epoch</th>\n",
       "      <th>tuner/bracket</th>\n",
       "      <th>tuner/round</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>True</td>\n",
       "      <td>0.102735</td>\n",
       "      <td>2</td>\n",
       "      <td>455</td>\n",
       "      <td>0.319693</td>\n",
       "      <td>439</td>\n",
       "      <td>0.205902</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   dropout  dropout_first  num_layers  dense_1  dropout_1  dense_2  dropout_2  \\\n",
       "0     True       0.102735           2      455   0.319693      439   0.205902   \n",
       "\n",
       "   tuner/epochs  tuner/initial_epoch  tuner/bracket  tuner/round  \n",
       "0             4                    0              0            0  "
      ]
     },
     "execution_count": 502,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(best_hyperparameters.values,index=[0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Meilleur nombre d'epochs 📅"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Cependant le nombre d'épochs étant défini à la main pour pouvoir explorer les hypers paramètres dans un temps raisonable, il est encore possible d'entrainer le modèle.\n",
    "\n",
    "Nous allons donc entrainer notre modèle jusqu'a ce que la fonction d'early stoping s'active :"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "best_model_epochs_section_label = best_model_section_label + \"-epochs\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def fit(model, x, y, epochs, section_label, validation_split=0.2):\n",
    "    log_dir = get_log_directory(section_label)\n",
    "    \n",
    "    early_stoping_callback = get_early_stoping()\n",
    "    tensorboard_callback = get_tensorboard_callback(log_dir)\n",
    "    callbacks = [tensorboard_callback, early_stoping_callback]\n",
    "\n",
    "    model.fit(\n",
    "        x,\n",
    "        y,\n",
    "        epochs=epochs,\n",
    "        batch_size=256,\n",
    "        validation_split=validation_split,\n",
    "        callbacks=callbacks)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/60\n",
      "278/278 [==============================] - 19s 69ms/step - loss: 0.4985 - accuracy: 0.6149 - val_loss: 0.3174 - val_accuracy: 0.9019\n",
      "Epoch 2/60\n",
      "278/278 [==============================] - 23s 81ms/step - loss: 0.4797 - accuracy: 0.6200 - val_loss: 0.3061 - val_accuracy: 0.9057\n",
      "Epoch 3/60\n",
      "278/278 [==============================] - 22s 78ms/step - loss: 0.4583 - accuracy: 0.6272 - val_loss: 0.2948 - val_accuracy: 0.9049\n",
      "Epoch 4/60\n",
      "278/278 [==============================] - 21s 76ms/step - loss: 0.4405 - accuracy: 0.6298 - val_loss: 0.2845 - val_accuracy: 0.9084\n",
      "Epoch 5/60\n",
      "278/278 [==============================] - 21s 75ms/step - loss: 0.4264 - accuracy: 0.6341 - val_loss: 0.2804 - val_accuracy: 0.9105\n",
      "Epoch 6/60\n",
      "278/278 [==============================] - 21s 76ms/step - loss: 0.4056 - accuracy: 0.6392 - val_loss: 0.2716 - val_accuracy: 0.9120\n",
      "Epoch 7/60\n",
      "278/278 [==============================] - 21s 76ms/step - loss: 0.3961 - accuracy: 0.6429 - val_loss: 0.2637 - val_accuracy: 0.9136\n",
      "Epoch 8/60\n",
      "278/278 [==============================] - 21s 76ms/step - loss: 0.3840 - accuracy: 0.6465 - val_loss: 0.2561 - val_accuracy: 0.9068\n",
      "Epoch 9/60\n",
      "278/278 [==============================] - 22s 78ms/step - loss: 0.3722 - accuracy: 0.6491 - val_loss: 0.2528 - val_accuracy: 0.9154\n",
      "Epoch 10/60\n",
      "278/278 [==============================] - 21s 76ms/step - loss: 0.3575 - accuracy: 0.6539 - val_loss: 0.2473 - val_accuracy: 0.9117\n",
      "Epoch 11/60\n",
      "278/278 [==============================] - 21s 77ms/step - loss: 0.3459 - accuracy: 0.6549 - val_loss: 0.2391 - val_accuracy: 0.6602\n",
      "Epoch 12/60\n",
      "278/278 [==============================] - 22s 78ms/step - loss: 0.3378 - accuracy: 0.6580 - val_loss: 0.2390 - val_accuracy: 0.9179\n",
      "Epoch 13/60\n",
      "278/278 [==============================] - 22s 79ms/step - loss: 0.3288 - accuracy: 0.6596 - val_loss: 0.2394 - val_accuracy: 0.6638\n",
      "Epoch 14/60\n",
      "278/278 [==============================] - 20s 72ms/step - loss: 0.3186 - accuracy: 0.6623 - val_loss: 0.2360 - val_accuracy: 0.6630\n",
      "Epoch 15/60\n",
      "278/278 [==============================] - 21s 75ms/step - loss: 0.3098 - accuracy: 0.6662 - val_loss: 0.2246 - val_accuracy: 0.9207\n",
      "Epoch 16/60\n",
      "278/278 [==============================] - 21s 74ms/step - loss: 0.3015 - accuracy: 0.6668 - val_loss: 0.2222 - val_accuracy: 0.6640\n",
      "Epoch 17/60\n",
      "278/278 [==============================] - 21s 74ms/step - loss: 0.2926 - accuracy: 0.6715 - val_loss: 0.2204 - val_accuracy: 0.6642\n",
      "Epoch 18/60\n",
      "278/278 [==============================] - 20s 74ms/step - loss: 0.2832 - accuracy: 0.6707 - val_loss: 0.2219 - val_accuracy: 0.6645\n",
      "Epoch 19/60\n",
      "278/278 [==============================] - 20s 73ms/step - loss: 0.2747 - accuracy: 0.6740 - val_loss: 0.2190 - val_accuracy: 0.6648\n",
      "Epoch 20/60\n",
      "278/278 [==============================] - 21s 74ms/step - loss: 0.2701 - accuracy: 0.6749 - val_loss: 0.2173 - val_accuracy: 0.6644\n",
      "Epoch 21/60\n",
      "278/278 [==============================] - 20s 73ms/step - loss: 0.2637 - accuracy: 0.6772 - val_loss: 0.2104 - val_accuracy: 0.6680\n",
      "Epoch 22/60\n",
      "278/278 [==============================] - 20s 74ms/step - loss: 0.2595 - accuracy: 0.6790 - val_loss: 0.2151 - val_accuracy: 0.6655\n",
      "Epoch 23/60\n",
      "278/278 [==============================] - 21s 74ms/step - loss: 0.2525 - accuracy: 0.6795 - val_loss: 0.2109 - val_accuracy: 0.6670\n",
      "Epoch 24/60\n",
      "278/278 [==============================] - 20s 73ms/step - loss: 0.2443 - accuracy: 0.6826 - val_loss: 0.2077 - val_accuracy: 0.6668\n",
      "Epoch 25/60\n",
      "278/278 [==============================] - 21s 75ms/step - loss: 0.2380 - accuracy: 0.6837 - val_loss: 0.2061 - val_accuracy: 0.6680\n",
      "Epoch 26/60\n",
      "278/278 [==============================] - 21s 75ms/step - loss: 0.2329 - accuracy: 0.6856 - val_loss: 0.2033 - val_accuracy: 0.6673\n",
      "Epoch 27/60\n",
      "278/278 [==============================] - 20s 74ms/step - loss: 0.2288 - accuracy: 0.6860 - val_loss: 0.2050 - val_accuracy: 0.6675\n",
      "Epoch 28/60\n",
      "278/278 [==============================] - 20s 74ms/step - loss: 0.2237 - accuracy: 0.6869 - val_loss: 0.2057 - val_accuracy: 0.6663\n",
      "Epoch 29/60\n",
      "278/278 [==============================] - 21s 75ms/step - loss: 0.2173 - accuracy: 0.6897 - val_loss: 0.2002 - val_accuracy: 0.6699\n",
      "Epoch 30/60\n",
      "278/278 [==============================] - 21s 74ms/step - loss: 0.2221 - accuracy: 0.6869 - val_loss: 0.2089 - val_accuracy: 0.6665\n",
      "Epoch 31/60\n",
      "278/278 [==============================] - 21s 75ms/step - loss: 0.2169 - accuracy: 0.6889 - val_loss: 0.2021 - val_accuracy: 0.6671\n",
      "Epoch 32/60\n",
      "278/278 [==============================] - 22s 78ms/step - loss: 0.2122 - accuracy: 0.6890 - val_loss: 0.2016 - val_accuracy: 0.6677\n",
      "Epoch 33/60\n",
      "278/278 [==============================] - 21s 76ms/step - loss: 0.2099 - accuracy: 0.6902 - val_loss: 0.2034 - val_accuracy: 0.6673\n"
     ]
    }
   ],
   "source": [
    "fit(best_model, x_train_fixed, y_train_fixed, 60, best_model_epochs_section_label)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "116/116 [==============================] - 1s 7ms/step - loss: 0.2936 - accuracy: 0.9097\n",
      "Test loss: 0.29361987113952637\n",
      "Test accuracy: 0.9097297191619873\n"
     ]
    }
   ],
   "source": [
    "evaluate(best_model, x_test_fixed, y_test_fixed)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "On peut voir que d'après la fonction d'`EarlyStopping` le nombre d'`epochs` optimal est `33`.\n",
    "\n",
    "On peut également noter une valeur de `test accuracy` correcte.\n",
    "\n",
    "On peut cependant noter que la meilleure `accuracy` d'évaluation correspond à `9` `epochs`. Il aurait donc fallu entraîner notre modèle sur `9` `epochs` pour obtenir de meilleurs performances."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 2 Des chiffres et des lettres"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Chargement des données 💾"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0, 1, 2, 3, 4, 5, 6, 7, 8, 9]\n",
      "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/mnist.npz\n",
      "11490434/11490434 [==============================] - 2s 0us/step\n"
     ]
    }
   ],
   "source": [
    "from tensorflow.keras.datasets import mnist\n",
    "\n",
    "from matplotlib import pyplot as plt\n",
    "\n",
    "numbers = list(range(0, 10))\n",
    "print(numbers)\n",
    "\n",
    "# load dataset\n",
    "(x_train_number_unprocessed, y_train_number_unprocessed), (x_test_number_unprocessed, y_test_number_unprocessed) = mnist.load_data()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train: x=(60000, 28, 28), y=(60000, 10)\n",
      "Test: x=(10000, 28, 28), y=(10000, 10)\n"
     ]
    }
   ],
   "source": [
    "(x_train_number, y_train_number) = preprocess_data(x_train_number_unprocessed, y_train_number_unprocessed, 10)\n",
    "(x_test_number, y_test_number) = preprocess_data(x_test_number_unprocessed, y_test_number_unprocessed, 10)\n",
    "\n",
    "print_shape(x_train_number, y_train_number, x_test_number, y_test_number)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Exploration 🧭"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 700x700 with 9 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_9_random_images(x_train_number, y_train_number, numbers)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 900x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_train_vs_test(y_train_number, y_test_number, numbers)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Numbers:\n",
      "Train: x=(60000, 28, 28), y=(60000, 10)\n",
      "Test: x=(10000, 28, 28), y=(10000, 10)\n",
      "Letters:\n",
      "Train: x=(88800, 28, 28), y=(88800, 19)\n",
      "Test: x=(14800, 28, 28), y=(14800, 19)\n"
     ]
    }
   ],
   "source": [
    "print(\"Numbers:\")\n",
    "print_shape(x_train_number, y_train_number, x_test_number, y_test_number)\n",
    "print(\"Letters:\")\n",
    "print_shape(x_train_fixed, y_train_fixed, x_test_fixed, y_test_fixed)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Fusion 🔀 et preprocess des données 🧮"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Vu que l'on ne veut classer que dans 2 catégories, ils nous faut mettre à jour les labels de nos données."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "number_or_letter = [\"letters\", \"number\"]\n",
    "\n",
    "def preprocess_data_number_letters(x, y, categorical_count=26):    \n",
    "    x = shape_data(x)\n",
    "    y = categorize_data(y, categorical_count)\n",
    "    return x, y\n",
    "\n",
    "def reclass(y, is_fist_class: bool = True):\n",
    "    if is_fist_class:\n",
    "        return np.zeros(y.shape)\n",
    "    else:10000, 28, 28), y=(10000, 10\n",
    "        return np.ones(y.shape)\n",
    "\n",
    "def merge_letters_and_numbers(letter, number):\n",
    "    return np.concatenate((\n",
    "    letter,\n",
    "    number))\n",
    "\n",
    "def preprocess_letters_and_numbers(\n",
    "    x_letter,\n",
    "    x_number,\n",
    "    y_letter,\n",
    "    y_number,\n",
    "):\n",
    "    # Reclass letters\n",
    "    y_letter_reclassified = reclass(y_letter, True)\n",
    "    # Reclass numbers\n",
    "    y_number_reclassified = reclass(y_number, False)\n",
    "    \n",
    "    x_letter_shaped = shape_data(x_letter)\n",
    "    x_number_shaped = shape_data(x_number)\n",
    "\n",
    "    # Merge numbers and letters\n",
    "    # train\n",
    "    x = merge_letters_and_numbers(\n",
    "        x_letter_shaped,\n",
    "        x_number_shaped)\n",
    "\n",
    "    y_number_letter_uncategorized = merge_letters_and_numbers(\n",
    "        y_letter_reclassified,\n",
    "        y_number_reclassified)\n",
    "\n",
    "    y = categorize_data(y_number_letter_uncategorized, 2)\n",
    "    return x, y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((148800, 2), (74800, 2))"
      ]
     },
     "execution_count": 479,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train_letter_number.shape, y_test_letter_number.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train_letter_number, y_train_letter_number = preprocess_letters_and_numbers(\n",
    "    x_train_unprocessed,\n",
    "    x_train_number_unprocessed,\n",
    "    y_train_unprocessed,\n",
    "    y_train_number_unprocessed)\n",
    "x_test_letter_number, y_test_letter_number = preprocess_letters_and_numbers(\n",
    "    x_test_unprocessed,\n",
    "    x_test_number_unprocessed,\n",
    "    y_test_unprocessed,\n",
    "    y_test_number_unprocessed)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 700x700 with 9 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_9_random_images(x_train_letter_number, y_train_letter_number, number_or_letter)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 900x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_train_vs_test(y_train_letter_number, y_test_letter_number, number_or_letter)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "On remarque une différence entre le nombre de données d'entraînement pour les lettres et pour les chiffres qui déséquilibre un peu le jeu de données. Cela reste cependant cohérent par rapport au nombre de lettre (19) et de chiffres (10) qui constituent notre jeu de données."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Modèle 🚀"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Nous allons utiliser la meme méthode que dans la partie 1.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "letter-number-tunner .cache/letter-number-tunner/log\n"
     ]
    }
   ],
   "source": [
    "letter_number_section_label = \"letter-number-tunner\"\n",
    "letter_number_log_dir = get_log_directory(letter_number_section_label)\n",
    "print(letter_number_section_label, letter_number_log_dir)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "784 2\n"
     ]
    }
   ],
   "source": [
    "num_classes = len(number_or_letter)\n",
    "print(num_pixels, num_classes)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "toc-hr-collapsed": true
   },
   "source": [
    "#### Meilleurs hyperparamètres 🥇"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def build_model_letter_number(hp, num_pixels=num_pixels, num_classes=num_classes):\n",
    "    return build_model(hp, num_pixels, num_classes=num_classes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Trial 10 Complete [00h 15m 43s]\n",
      "val_accuracy: 0.9999327659606934\n",
      "\n",
      "Best val_accuracy So Far: 1.0\n",
      "Total elapsed time: 01h 32m 35s\n",
      "INFO:tensorflow:Oracle triggered exit\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras_tuner.tuners.hyperband.Hyperband at 0x7f3405cb63e0>"
      ]
     },
     "execution_count": 494,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tunner_letter_number = get_hyperband_tunner(\n",
    "    letter_number_section_label,\n",
    "    max_epochs=6,\n",
    "    build_model=build_model_letter_number)\n",
    "\n",
    "benchmark(\n",
    "    tunner_letter_number,\n",
    "    letter_number_log_dir,\n",
    "    x_train_letter_number,\n",
    "    y_train_letter_number)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "best_model_letter_number = tunner_letter_number.get_best_models()[0]\n",
    "best_hyperparameters_letter_number = tunner_letter_number.get_best_hyperparameters()[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "194/194 [==============================] - 2s 8ms/step - loss: 0.0047 - accuracy: 0.9990\n",
      "Test loss: 0.004693709313869476\n",
      "Test accuracy: 0.9989516139030457\n"
     ]
    }
   ],
   "source": [
    "score = best_model_letter_number.evaluate(\n",
    "    x_test_letter_number,\n",
    "    y_test_letter_number,\n",
    "    batch_size=128\n",
    ")\n",
    "\n",
    "print(\"Test loss:\", score[0])\n",
    "print(\"Test accuracy:\", score[1])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "On peut voir que l'`accuracy` d'évaluation de notre modèle est excellente.\n",
    "\n",
    "Nous pourrions l'entrainer davantage afin de selectioner le meilleur nombre d'`epochs`, cependant nous n'en navons malheureusement pas eu le temps.\n",
    "\n",
    "Il aurait fallu pour cela laisser la méthode d'`EarlyStopping` arrêter l'entraînement comme fait dans la partie 1."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Detecting that an object or model or tf.train.Checkpoint is being deleted with unrestored values. See the following logs for the specific values in question. To silence these warnings, use `status.expect_partial()`. See https://www.tensorflow.org/api_docs/python/tf/train/Checkpoint#restorefor details about the status object returned by the restore function.\n",
      "WARNING:tensorflow:Value in checkpoint could not be found in the restored object: (root).optimizer._variables.1\n",
      "WARNING:tensorflow:Value in checkpoint could not be found in the restored object: (root).optimizer._variables.2\n",
      "WARNING:tensorflow:Value in checkpoint could not be found in the restored object: (root).optimizer._variables.3\n",
      "WARNING:tensorflow:Value in checkpoint could not be found in the restored object: (root).optimizer._variables.4\n",
      "WARNING:tensorflow:Value in checkpoint could not be found in the restored object: (root).optimizer._variables.5\n",
      "WARNING:tensorflow:Value in checkpoint could not be found in the restored object: (root).optimizer._variables.6\n",
      "WARNING:tensorflow:Value in checkpoint could not be found in the restored object: (root).optimizer._variables.7\n",
      "WARNING:tensorflow:Value in checkpoint could not be found in the restored object: (root).optimizer._variables.8\n",
      "WARNING:tensorflow:Value in checkpoint could not be found in the restored object: (root).optimizer._variables.9\n",
      "WARNING:tensorflow:Value in checkpoint could not be found in the restored object: (root).optimizer._variables.10\n",
      "WARNING:tensorflow:Value in checkpoint could not be found in the restored object: (root).optimizer._variables.11\n",
      "WARNING:tensorflow:Value in checkpoint could not be found in the restored object: (root).optimizer._variables.12\n",
      "WARNING:tensorflow:Value in checkpoint could not be found in the restored object: (root).optimizer._variables.13\n",
      "WARNING:tensorflow:Value in checkpoint could not be found in the restored object: (root).optimizer._variables.14\n",
      "WARNING:tensorflow:Value in checkpoint could not be found in the restored object: (root).optimizer._variables.15\n",
      "WARNING:tensorflow:Value in checkpoint could not be found in the restored object: (root).optimizer._variables.16\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 503,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "best_model_letter_number_plot = tunner_letter_number.get_best_models()[0]\n",
    "plot_model(best_model_letter_number_plot, to_file=\"letter-number-model.png\", show_shapes=True, show_layer_names=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "On peut noter que le modèle le plus efficace à une architecture similaire à celle de du modèle de classification des lettres."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>dropout</th>\n",
       "      <th>dropout_first</th>\n",
       "      <th>num_layers</th>\n",
       "      <th>dense_1</th>\n",
       "      <th>dropout_1</th>\n",
       "      <th>dense_2</th>\n",
       "      <th>dropout_2</th>\n",
       "      <th>tuner/epochs</th>\n",
       "      <th>tuner/initial_epoch</th>\n",
       "      <th>tuner/bracket</th>\n",
       "      <th>tuner/round</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>True</td>\n",
       "      <td>0.40758</td>\n",
       "      <td>2</td>\n",
       "      <td>360</td>\n",
       "      <td>0.230986</td>\n",
       "      <td>502</td>\n",
       "      <td>0.10884</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   dropout  dropout_first  num_layers  dense_1  dropout_1  dense_2  dropout_2  \\\n",
       "0     True        0.40758           2      360   0.230986      502    0.10884   \n",
       "\n",
       "   tuner/epochs  tuner/initial_epoch  tuner/bracket  tuner/round  \n",
       "0             6                    0              0            0  "
      ]
     },
     "execution_count": 504,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(best_hyperparameters_letter_number.values,index=[0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Comparons les hyper paramètres actuels, et ceux du modèle de classification des lettres:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th></th>\n",
       "      <th>Letters and numbers</th>\n",
       "      <th>Letters</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>dropout</th>\n",
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       "      <td>360</td>\n",
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      "text/plain": [
       "                    Letters and numbers   Letters\n",
       "dropout                            True      True\n",
       "dropout_first                   0.40758  0.102735\n",
       "num_layers                            2         2\n",
       "dense_1                             360       455\n",
       "dropout_1                      0.230986  0.319693\n",
       "dense_2                             502       439\n",
       "dropout_2                       0.10884  0.205902\n",
       "tuner/epochs                          6         4\n",
       "tuner/initial_epoch                   0         0\n",
       "tuner/bracket                         0         0\n",
       "tuner/round                           0         0"
      ]
     },
     "execution_count": 508,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame({ \"Letters and numbers\": best_hyperparameters_letter_number.values, \"Letters\": best_hyperparameters.values})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Nous pouvons voir que le nombre de couches dense cachées supplémentaires est égal pour les 2 modèles. Ce qui est une conclusion attendu, les 2 modèles étant similaires, travaillant sur des données de qualités égales et utilisant le même tuner keras avec la même graine d'aléatoire.\n",
    "\n",
    "Nous noterons également que le nombre de neurones dans ces couches cachées se situe dans des ordres de grandeurs similaires, probablement pour les raisons évoquées ci-dessus."
   ]
  },
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   "metadata": {},
   "outputs": [],
   "source": []
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